{"id":"W4401108292","doi":"10.1007/s43657-024-00157-x","title":"Associations of Plasma Lipidomic Profiles with Uric Acid and Hyperuricemia Risk in Middle-Aged and Elderly Chinese","year":2024,"lang":"en","type":"article","venue":"Phenomics","topic":"Gout, Hyperuricemia, Uric Acid","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Lipidomics; Hyperuricemia; Internal medicine; Blood lipids; Endocrinology; Lipid metabolism; Population; Diacylglycerol kinase; Uric acid; Lipidome; Lipogenesis; Chemistry; Medicine; Biology; Cholesterol; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003167223,0.0002298262,0.0004809547,0.0002505481,0.00006385087,0.00004186078,0.00007579122,0.0001370527,0.00001398487],"category_scores_gemma":[0.000245885,0.0001780475,0.00004515232,0.0004449563,0.0001183036,0.0001463723,0.00006532837,0.0004531628,0.000007838003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001432585,"about_ca_system_score_gemma":0.000164502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001756824,"about_ca_topic_score_gemma":0.0001266617,"domain_scores_codex":[0.9987444,0.00005730894,0.0003723376,0.0003769161,0.0001901753,0.0002588049],"domain_scores_gemma":[0.9991471,0.0003052565,0.0001392304,0.0002485579,0.00003828387,0.000121572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003768149,0.0002294926,0.949194,0.0006960711,0.0004776574,0.00005773663,0.008059707,0.00006377076,0.03262603,0.0002338636,0.0002264714,0.007758412],"study_design_scores_gemma":[0.006296217,0.0008399319,0.975516,0.000516243,0.001014273,0.0002969933,0.001071392,0.01010018,0.002406814,0.001025285,0.000386941,0.0005296803],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950514,0.002925005,0.00006110073,0.0002721981,0.000117351,0.0004950709,0.0001246507,0.00007332836,0.0008798922],"genre_scores_gemma":[0.9954981,0.0005894335,0.003368592,0.00004966309,0.0001727002,0.0000295695,0.00003887108,0.00005082018,0.0002022812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03021921,"threshold_uncertainty_score":0.7260565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361772979963522,"score_gpt":0.2391020791031158,"score_spread":0.2254843493034805,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}